Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction. Export

@article{citeulike:4373189, abstract = {BACKGROUND:Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites. RESULTS:We characterized the spatial context of phosphorylation sites and assessed its usability for improving phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with 3D-structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines and phosphotyrosines to extract signature 3D-profiles. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. When compared the sequence-only based predictors developed as part of this study and to other, publicly available predictors, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information. CONCLUSIONS:While sequence-only based methods were observed to harbor most of the discriminatory information with regard to phosphorylation, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites.}, author = {Durek, Pawel and Schudoma, Christian and Weckwerth, Wolfram and Selbig, Joachim and Walther, Dirk}, citeulike-article-id = {4373189}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-10-117}, doi = {10.1186/1471-2105-10-117}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {phosphoproteomics, phosphorylation}, number = {1}, pages = {117+}, posted-at = {2009-05-31 19:20:06}, priority = {2}, title = {Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction.}, url = {http://dx.doi.org/10.1186/1471-2105-10-117}, volume = {10}, year = {2009} }

TY - JOUR ID - citeulike:4373189 L3 - citeulike-article-id:4373189 N2 - BACKGROUND:Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites. RESULTS:We characterized the spatial context of phosphorylation sites and assessed its usability for improving phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with 3D-structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines and phosphotyrosines to extract signature 3D-profiles. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. When compared the sequence-only based predictors developed as part of this study and to other, publicly available predictors, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information. CONCLUSIONS:While sequence-only based methods were observed to harbor most of the discriminatory information with regard to phosphorylation, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction. VL - 10 SP - 117 KW - phosphoproteomics KW - phosphorylation AU - Durek, Pawel AU - Schudoma, Christian AU - Weckwerth, Wolfram AU - Selbig, Joachim AU - Walther, Dirk PY - 2009/// UR - http://dx.doi.org/10.1186/1471-2105-10-117 ER -